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Machine Learning for SQL-Based Anomaly Detection & Fraud Analytics in Financial Data

Author(s) Raghavender Maddali
Country India
Abstract The rapid evolution of financial transactions has made it imperative to introduce strong fraud detection mechanisms that provide security and regulatory compliance in financial systems. In this paper, SAFE, a Scalable Automatic Feature Engineering framework for industrial applications, is introduced to improve real-time fraud detection in transactional data. Through machine learning-based SQL anomaly detection, SAFE combines pattern detection, outlier detection, and predictive modeling to detect suspicious activity. The design maximizes feature extraction and selection, enhancing fraud analytics accuracy and efficiency. With real-time data processing, SAFE facilitates proactive risk management, minimizing false positives and maximizing fraud detection performance. The research compares SAFE on various industrial data sets, proving its scalability and flexibility across financial sectors. Comparing SAFE with conventional fraud detection methods proves its enhanced detection ratio, computational effectiveness, and ability to adapt to emerging fraud patterns. The study emphasizes the need for automated feature engineering in the assurance of financial safety and compliance to regulations during web transactions.
Keywords Fraud detection, anomaly detection, machine learning, financial activity, SQL reporting, predictive models, feature crafting, risk elimination, real-time analysis, security finance.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 2, Issue 10, October 2021
Published On 2021-10-05
Cite This Machine Learning for SQL-Based Anomaly Detection & Fraud Analytics in Financial Data - Raghavender Maddali - IJLRP Volume 2, Issue 10, October 2021. DOI 10.5281/zenodo.15107543
DOI https://doi.org/10.5281/zenodo.15107543
Short DOI https://doi.org/g8986k
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